SatHealth: A Multimodal Public Health Dataset with Satellite-based Environmental Factors
This work addresses a data gap for public health researchers and AI practitioners, enabling better incorporation of environmental factors into healthcare models, though it is incremental as it builds on existing multimodal data approaches.
The authors tackled the lack of long-term, fine-grained environmental data in public health studies by developing SatHealth, a multimodal dataset combining satellite-based environmental factors, disease prevalences, and social determinants, which improved AI model performance and generalizability in regional and personal health tasks.
Living environments play a vital role in the prevalence and progression of diseases, and understanding their impact on patient's health status becomes increasingly crucial for developing AI models. However, due to the lack of long-term and fine-grained spatial and temporal data in public and population health studies, most existing studies fail to incorporate environmental data, limiting the models' performance and real-world application. To address this shortage, we developed SatHealth, a novel dataset combining multimodal spatiotemporal data, including environmental data, satellite images, all-disease prevalences estimated from medical claims, and social determinants of health (SDoH) indicators. We conducted experiments under two use cases with SatHealth: regional public health modeling and personal disease risk prediction. Experimental results show that living environmental information can significantly improve AI models' performance and temporal-spatial generalizability on various tasks. Finally, we deploy a web-based application to provide an exploration tool for SatHealth and one-click access to both our data and regional environmental embedding to facilitate plug-and-play utilization. SatHealth is now published with data in Ohio, and we will keep updating SatHealth to cover the other parts of the US. With the web application and published code pipeline, our work provides valuable angles and resources to include environmental data in healthcare research and establishes a foundational framework for future research in environmental health informatics.